Spatiotemporal variability of drought and the potential climatological driving factors in the Liao River basin

Liu, Xingcai; Xu, Zongxue; Yu, Ruihong

doi:10.1002/hyp.8104

Cited by 23 publications

(10 citation statements)

References 57 publications

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“…The SPI is an index based on the probability distribution of a long‐term precipitation time series (McKee et al ., , ). The calculation of SPI is presented as follows (Liu et al ., ): It is first fitted with an incomplete gamma probability density function to a given frequency distribution of precipitation at a station.

p (italicθ) = \frac{1}{b^{a} Γ (a)} {italicθ}^{a - 1} e^{- θ / b}, θ > 0

where θ is the precipitation amount, a is the shape parameter, b is the scale parameter, and the gamma function is expressed as:

Γ (a) = \int_{0}^{\infty} θ^{a - 1} e^{- italicθ} dθ

…”

Section: Methodsmentioning

confidence: 97%

“…The maximum likelihood method is used to estimate the optimal values of a and b (Liu et al ., ).

{\begin{cases} a = \frac{1}{4 A} true(1 + \sqrt{1 + \frac{4 A}{3}} true) \\ b = \frac{\overset{true¯}{italicθ}}{a} \\ A = normalIn true(\overset{θ}{true} true) - \frac{\sum In (italicθ)}{n} \end{cases}

where n is the number of precipitation records.…”

Section: Methodsmentioning

confidence: 97%

“…The SPI can be used to monitor dry or wet conditions on a variety of time scales. This temporal flexibility allows the SPI to be useful in both short‐term agricultural and long‐term hydrological applications (Liu et al ., ; Tabari et al ., ).…”

Section: Introductionmentioning

confidence: 99%

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Spatiotemporal analysis of climate variability (1971–2010) in spring and summer on the Loess Plateau, China

Zhang

Zhao

et al. 2013

Hydrological Processes

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By using the Variable Infiltration Capacity model with Palmer Drought Severity Index (VIC‐PDSI) model and Standardized Precipitation Index (SPI), spatiotemporal trends of climate variation during the main growing seasons for plants of Loess Plateau between 1971 and 2010 were detected and characterized. The VIC‐PDSI model is established by combining the VIC model with PDSI. The simulation results and the grids system of VIC were applied to substitute for the two‐layer bucket‐type model to do the hydrological accounting, which could improve the physical mechanism of PDSI and expand its application range. Our results suggest that the climate of the study area has experienced a drying and warming trend during the past four decades. Apart from some individual years and regions, there was a perpetuation of water deficit over the Plateau both in spring and summer. The drought frequency increased from southeast to northwest in spring, while the drought frequency decreased from southeast to northwest in summer. The climate in the southern part of the Loess Plateau, accounting for 23.3% of the study region, showed a significant drying and warming trend in spring over the past four decades. The climate variability detected by VIC‐PDSI model shows good agreement with that monitored by SPI. Since a large part of the study region frequently suffered from water shortage during the main growing seasons for plants, people living in such drought‐prone areas should take measures to prevent the negative effects on agricultural production, reforestation, and regional food security caused by drought. Copyright © 2013 John Wiley & Sons, Ltd.

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p (italicθ) = \frac{1}{b^{a} Γ (a)} {italicθ}^{a - 1} e^{- θ / b}, θ > 0

where θ is the precipitation amount, a is the shape parameter, b is the scale parameter, and the gamma function is expressed as:

Γ (a) = \int_{0}^{\infty} θ^{a - 1} e^{- italicθ} dθ

…”

Section: Methodsmentioning

confidence: 97%

“…The maximum likelihood method is used to estimate the optimal values of a and b (Liu et al ., ).

{\begin{cases} a = \frac{1}{4 A} true(1 + \sqrt{1 + \frac{4 A}{3}} true) \\ b = \frac{\overset{true¯}{italicθ}}{a} \\ A = normalIn true(\overset{θ}{true} true) - \frac{\sum In (italicθ)}{n} \end{cases}

where n is the number of precipitation records.…”

Section: Methodsmentioning

confidence: 97%

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Spatiotemporal analysis of climate variability (1971–2010) in spring and summer on the Loess Plateau, China

Zhang

Zhao

et al. 2013

Hydrological Processes

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“…The SPI is easy to apply, and has various advantages, as discussed by Hayes et al (1999). Therefore, the SPI has been widely used to investigate meteorological droughts in many countries, including Australia (Wen et al 2011), Canada (Quiring and Papakryiakou 2003), China (Wu et al 2001;Liu et al 2012), Greece (Tsakiris and Vangelis 2004;Dimitrakopoulos et al 2011), India (Mishra and Desai 2005), Italy (Bonaccorso et al 2003;Piccarreta et al 2004), Spain (Lana et al 2001;Pasho et al 2011), Taiwan (Shiau 2006;Chen et al 2009), and the United States (Edwards and McKee 1997;Ji and Peters 2003).…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Drought Forecasting in Southern Taiwan Using El Niño-Southern Oscillation Index

Chen¹,

Yang

Kuo

et al. 2013

Terr. Atmos. Ocean. Sci.

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This study proposes a probabilistic drought forecasting model to forecast meteorological drought in Southern Taiwan using the El Niño-Southern Oscillation (ENSO) index. Meteorological drought is defined by the standardized precipitation index (SPI), and the ENSO index is El Niño sea surface temperature (SST). Two probabilistic forecasting model architectures were constructed based on the transition probabilities from El Niño SSTs to SPIs. Both model architectures forecast a one-month-ahead probability distribution for meteorological drought using different combinations of El Niño SST variables. Forecasting results showed the robustness of the probabilistic drought forecasting models. In addition, this study discussed the selection of El Niño SST variables used in the probabilistic drought forecasting model, and found that models with a single SST input outperformed those with multiple SST inputs.

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“…circulation effects on precipitation may have similar effects on drought [29]. Previous studies indicated that weaker East Asian Summer Monsoon (EASM) was the main cause responsible for decreasing northward propagation of water vapor flux, causing different wet (dry) tendency in South (North) China [30].…”

mentioning

confidence: 99%

Temporal-Spatial Variation of Drought Indicated by SPI and SPEI in Ningxia Hui Autonomous Region, China

2015

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Abstract:The Ningxia Hui Autonomous Region of China (Ningxia) is an important food production area in northwest China severely affected by drought. The Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) were calculated based on monthly meteorological data to explore climate change and variation in drought intensity, duration, frequency, and spatial extent in Ningxia during 1972-2011 show that the SPEI is more applicable than the SPI for exploring climate change and drought variation in Ningxia. The Ningxia climate experienced a significant drying tendency. Annual SPEI decreased about 0.37 decade −1 during 1972-2011. Drought was exacerbated by this drying tendency. Regional average duration, maximum duration, intensity, and frequency of drought identified by the SPEI increased by one month, three months, 0.15%, and 36.1%, respectively, during 1992-2011 compared to the period of 1972-1991. The spatial extent of drought identified by the SPEI increased about 14.4% decade −1 in the spring during 1972-2011. Spatially, drought frequency increased from north to south. Average intensity (maximum duration) of drought calculated by the SPEI increased (decreased) northward and southward from the central arid area.

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Spatiotemporal variability of drought and the potential climatological driving factors in the Liao River basin

Cited by 23 publications

References 57 publications

Spatiotemporal analysis of climate variability (1971–2010) in spring and summer on the Loess Plateau, China

Spatiotemporal analysis of climate variability (1971–2010) in spring and summer on the Loess Plateau, China

Probabilistic Drought Forecasting in Southern Taiwan Using El Niño-Southern Oscillation Index

Temporal-Spatial Variation of Drought Indicated by SPI and SPEI in Ningxia Hui Autonomous Region, China

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